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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">create_dl_model</span><span data-if="c" style="display:none;">T_create_dl_model</span><span data-if="cpp" style="display:none;">CreateDlModel</span><span data-if="dotnet" style="display:none;">CreateDlModel</span><span data-if="python" style="display:none;">create_dl_model</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">create_dl_model</span><span data-if="c" style="display:none;">T_create_dl_model</span><span data-if="cpp" style="display:none;">CreateDlModel</span><span data-if="dotnet" style="display:none;">CreateDlModel</span><span data-if="python" style="display:none;">create_dl_model</span></code> — Create a deep learning model.</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>create_dl_model</b>( :  : <a href="#OutputLayers"><i>OutputLayers</i></a> : <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="c" style="display:none;">
<p>
<code>Herror <b>T_create_dl_model</b>(const Htuple <a href="#OutputLayers"><i>OutputLayers</i></a>, Htuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>CreateDlModel</b>(const HTuple&amp; <a href="#OutputLayers"><i>OutputLayers</i></a>, HTuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const HDlLayerArray&amp; <a href="#OutputLayers"><i>OutputLayers</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const HDlLayer&amp; <a href="#OutputLayers"><i>OutputLayers</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>CreateDlModel</b>(const HDlLayerArray&amp; <a href="#OutputLayers"><i>OutputLayers</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>CreateDlModel</b>(const HDlLayer&amp; <a href="#OutputLayers"><i>OutputLayers</i></a>)</code></p>
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<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>CreateDlModel</b>(<a href="HTuple.html">HTuple</a> <a href="#OutputLayers"><i>outputLayers</i></a>, out <a href="HTuple.html">HTuple</a> <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>public <a href="HDlModel.html">HDlModel</a>(<a href="HDlLayer.html">HDlLayer[]</a> <a href="#OutputLayers"><i>outputLayers</i></a>)</code></p>
<p>
<code>public <a href="HDlModel.html">HDlModel</a>(<a href="HDlLayer.html">HDlLayer</a> <a href="#OutputLayers"><i>outputLayers</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>.<b>CreateDlModel</b>(<a href="HDlLayer.html">HDlLayer[]</a> <a href="#OutputLayers"><i>outputLayers</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>.<b>CreateDlModel</b>(<a href="HDlLayer.html">HDlLayer</a> <a href="#OutputLayers"><i>outputLayers</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>create_dl_model</b>(<a href="#OutputLayers"><i>output_layers</i></a>: MaybeSequence[HHandle]) -&gt; HHandle</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p>该算子 <code><span data-if="hdevelop" style="display:inline">create_dl_model</span><span data-if="c" style="display:none">create_dl_model</span><span data-if="cpp" style="display:none">CreateDlModel</span><span data-if="com" style="display:none">CreateDlModel</span><span data-if="dotnet" style="display:none">CreateDlModel</span><span data-if="python" style="display:none">create_dl_model</span></code> creates a deep learning model from a
graph and returns its handle in <a href="#DLModelHandle"><i><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></i></a>.
</p>
<p>A deep learning model in HALCON mainly consists of a directed acyclic
graph that defines the networks architecture.
Further components of a deep learning model in HALCON are parameters as
<i><span data-if="hdevelop" style="display:inline">'class_names'</span><span data-if="c" style="display:none">"class_names"</span><span data-if="cpp" style="display:none">"class_names"</span><span data-if="com" style="display:none">"class_names"</span><span data-if="dotnet" style="display:none">"class_names"</span><span data-if="python" style="display:none">"class_names"</span></i>, <i><span data-if="hdevelop" style="display:inline">'class_ids'</span><span data-if="c" style="display:none">"class_ids"</span><span data-if="cpp" style="display:none">"class_ids"</span><span data-if="com" style="display:none">"class_ids"</span><span data-if="dotnet" style="display:none">"class_ids"</span><span data-if="python" style="display:none">"class_ids"</span></i>, and many others,
or hyperparameters that are needed to train a model, as for example the
<i><span data-if="hdevelop" style="display:inline">'learning_rate'</span><span data-if="c" style="display:none">"learning_rate"</span><span data-if="cpp" style="display:none">"learning_rate"</span><span data-if="com" style="display:none">"learning_rate"</span><span data-if="dotnet" style="display:none">"learning_rate"</span><span data-if="python" style="display:none">"learning_rate"</span></i>.
While parameters and hyperparameters can be set after creation of the model
using <a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a>, the model itself can only be created
using <code><span data-if="hdevelop" style="display:inline">create_dl_model</span><span data-if="c" style="display:none">create_dl_model</span><span data-if="cpp" style="display:none">CreateDlModel</span><span data-if="com" style="display:none">CreateDlModel</span><span data-if="dotnet" style="display:none">CreateDlModel</span><span data-if="python" style="display:none">create_dl_model</span></code> if its network architecture is given in form of
a graph.
</p>
<p>To build a graph that defines the models network architecture, one needs to
put together the networks layers. In general, a graph starts with an input
layer. A subsequent layer that follows after the input layer uses the
input layer as feeding layer, and the new layer itself might be used as
a feeding layer for the next layer, and so on. This is repeated until
the graphs output layers (e.g., softmax or loss layers) are
appended to the graph.
To create a layer, use its specified creation operator, e.g., an input
layer is created using <a href="create_dl_layer_input.html"><code><span data-if="hdevelop" style="display:inline">create_dl_layer_input</span><span data-if="c" style="display:none">create_dl_layer_input</span><span data-if="cpp" style="display:none">CreateDlLayerInput</span><span data-if="com" style="display:none">CreateDlLayerInput</span><span data-if="dotnet" style="display:none">CreateDlLayerInput</span><span data-if="python" style="display:none">create_dl_layer_input</span></code></a>, a convolution layer
is created using <a href="create_dl_layer_convolution.html"><code><span data-if="hdevelop" style="display:inline">create_dl_layer_convolution</span><span data-if="c" style="display:none">create_dl_layer_convolution</span><span data-if="cpp" style="display:none">CreateDlLayerConvolution</span><span data-if="com" style="display:none">CreateDlLayerConvolution</span><span data-if="dotnet" style="display:none">CreateDlLayerConvolution</span><span data-if="python" style="display:none">create_dl_layer_convolution</span></code></a>, and so on.
</p>
<p>When the graph is defined, a model can be created using
<code><span data-if="hdevelop" style="display:inline">create_dl_model</span><span data-if="c" style="display:none">create_dl_model</span><span data-if="cpp" style="display:none">CreateDlModel</span><span data-if="com" style="display:none">CreateDlModel</span><span data-if="dotnet" style="display:none">CreateDlModel</span><span data-if="python" style="display:none">create_dl_model</span></code> by passing over the graphs output layer handles in
<a href="#OutputLayers"><i><code><span data-if="hdevelop" style="display:inline">OutputLayers</span><span data-if="c" style="display:none">OutputLayers</span><span data-if="cpp" style="display:none">OutputLayers</span><span data-if="com" style="display:none">OutputLayers</span><span data-if="dotnet" style="display:none">outputLayers</span><span data-if="python" style="display:none">output_layers</span></code></i></a>.
Note that the output layer handles save all other layers that directly or
indirectly serve as feeding input layers for the output layers during
their creation. This means that the output layer handles keep the
whole network architecture necessary for the creation of the model
using <code><span data-if="hdevelop" style="display:inline">create_dl_model</span><span data-if="c" style="display:none">create_dl_model</span><span data-if="cpp" style="display:none">CreateDlModel</span><span data-if="com" style="display:none">CreateDlModel</span><span data-if="dotnet" style="display:none">CreateDlModel</span><span data-if="python" style="display:none">create_dl_model</span></code>.
</p>
<p>The type of the created model, hence the task the model is designed for
(classification, object detection, segmentation), is only given by the
networks architecture.
However, if the networks architecture allows it, the type of the model,
<i><span data-if="hdevelop" style="display:inline">'type'</span><span data-if="c" style="display:none">"type"</span><span data-if="cpp" style="display:none">"type"</span><span data-if="com" style="display:none">"type"</span><span data-if="dotnet" style="display:none">"type"</span><span data-if="python" style="display:none">"type"</span></i>, can be set using <a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a>.
A specified model type allows a more user friendly usage in the HALCON
deep learning workflow.
Supported types are:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'generic'</span><span data-if="c" style="display:none">"generic"</span><span data-if="cpp" style="display:none">"generic"</span><span data-if="com" style="display:none">"generic"</span><span data-if="dotnet" style="display:none">"generic"</span><span data-if="python" style="display:none">"generic"</span></i>:</b></dt>
<dd><p>
 This is the default model type. The task the
model's neuronal network can solve is defined by its architecture.
When <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a> is applied for inference, 该算子
returns the activations of the output layers. To train the model
using <a href="train_dl_model_batch.html"><code><span data-if="hdevelop" style="display:inline">train_dl_model_batch</span><span data-if="c" style="display:none">train_dl_model_batch</span><span data-if="cpp" style="display:none">TrainDlModelBatch</span><span data-if="com" style="display:none">TrainDlModelBatch</span><span data-if="dotnet" style="display:none">TrainDlModelBatch</span><span data-if="python" style="display:none">train_dl_model_batch</span></code></a>, the underlying graph requires
loss layers.
</p></dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'classification'</span><span data-if="c" style="display:none">"classification"</span><span data-if="cpp" style="display:none">"classification"</span><span data-if="com" style="display:none">"classification"</span><span data-if="dotnet" style="display:none">"classification"</span><span data-if="python" style="display:none">"classification"</span></i>:</b></dt>
<dd>
<p>
 The model is specified for
classification and all layers required for training the model
are adapted to the model.
When <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a> is applied for inference,
the output is adapted according to the type, see <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a>
for more details.
See <a href="toc_deeplearning_classification.html">Deep Learning / Classification</a> for further information.</p>
<p>
In addition, 该算子 <a href="gen_dl_model_heatmap.html"><code><span data-if="hdevelop" style="display:inline">gen_dl_model_heatmap</span><span data-if="c" style="display:none">gen_dl_model_heatmap</span><span data-if="cpp" style="display:none">GenDlModelHeatmap</span><span data-if="com" style="display:none">GenDlModelHeatmap</span><span data-if="dotnet" style="display:none">GenDlModelHeatmap</span><span data-if="python" style="display:none">gen_dl_model_heatmap</span></code></a> can be used to
display the models heatmap.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'detection'</span><span data-if="c" style="display:none">"detection"</span><span data-if="cpp" style="display:none">"detection"</span><span data-if="com" style="display:none">"detection"</span><span data-if="dotnet" style="display:none">"detection"</span><span data-if="python" style="display:none">"detection"</span></i>:</b></dt>
<dd><p>
 The model is specified for object detection
and instance segmentation and all layers and anchors required for
training the  model are adapted to the model.
When <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a> is applied for inference,
the output is adapted according to the type, see <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a>
for more details.
See <a href="toc_deeplearning_objectdetection.html">Deep Learning / Object Detection and Instance Segmentation</a> for further information.
</p></dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'segmentation'</span><span data-if="c" style="display:none">"segmentation"</span><span data-if="cpp" style="display:none">"segmentation"</span><span data-if="com" style="display:none">"segmentation"</span><span data-if="dotnet" style="display:none">"segmentation"</span><span data-if="python" style="display:none">"segmentation"</span></i>:</b></dt>
<dd><p>
 The model is specified for semantic
segmentation or edge extraction respectively and all layers required for
training the model are adapted to the model.
When <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a> is applied for inference,
the output is adapted according to the type, see <a href="apply_dl_model.html"><code><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></code></a>
for more details.
See <a href="toc_deeplearning_semanticsegmentation.html">Deep Learning / Semantic Segmentation and Edge Extraction</a> for further information.
</p></dd>
</dl>
<p>
Furthermore, many deep learning procedures provide more functionality for the
model if its type is set. As an example, <code>dev_display_dl_data</code>
can be used to display the inferred results more nicely.
</p>
<p>Note that setting a model type requires that the graph fulfills certain
structure conditions. We recommend to follow the architecture of our
delivered neuronal networks if the model type should be set to one of these
types.
</p>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  <li>未经并行化处理。</li>
</ul>
<p>This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.</p>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="OutputLayers" class="parname"><b><code><span data-if="hdevelop" style="display:inline">OutputLayers</span><span data-if="c" style="display:none">OutputLayers</span><span data-if="cpp" style="display:none">OutputLayers</span><span data-if="com" style="display:none">OutputLayers</span><span data-if="dotnet" style="display:none">outputLayers</span><span data-if="python" style="display:none">output_layers</span></code></b> (input_control)  </span><span>dl_layer(-array) <code>→</code> <span data-if="dotnet" style="display:none"><a href="HDlLayer.html">HDlLayer</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">MaybeSequence[HHandle]</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Output layers of the graph.</p>
</div>
  <div class="par">
<div class="parhead">
<span id="DLModelHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></b> (output_control)  </span><span>dl_model <code>→</code> <span data-if="dotnet" style="display:none"><a href="HDlModel.html">HDlModel</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Handle of the deep learning model.</p>
</div>
<h2 id="sec_result">结果</h2>
<p>如果参数均有效，算子 <code><span data-if="hdevelop" style="display:inline">create_dl_model</span><span data-if="c" style="display:none">create_dl_model</span><span data-if="cpp" style="display:none">CreateDlModel</span><span data-if="com" style="display:none">CreateDlModel</span><span data-if="dotnet" style="display:none">CreateDlModel</span><span data-if="python" style="display:none">create_dl_model</span></code>
返回值 <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
    . 如有必要，将引发异常。</p>
<h2 id="sec_predecessors">可能的前置算子</h2>
<p>
<code><a href="create_dl_layer_softmax.html"><span data-if="hdevelop" style="display:inline">create_dl_layer_softmax</span><span data-if="c" style="display:none">create_dl_layer_softmax</span><span data-if="cpp" style="display:none">CreateDlLayerSoftmax</span><span data-if="com" style="display:none">CreateDlLayerSoftmax</span><span data-if="dotnet" style="display:none">CreateDlLayerSoftmax</span><span data-if="python" style="display:none">create_dl_layer_softmax</span></a></code>, 
<code><a href="create_dl_layer_loss_cross_entropy.html"><span data-if="hdevelop" style="display:inline">create_dl_layer_loss_cross_entropy</span><span data-if="c" style="display:none">create_dl_layer_loss_cross_entropy</span><span data-if="cpp" style="display:none">CreateDlLayerLossCrossEntropy</span><span data-if="com" style="display:none">CreateDlLayerLossCrossEntropy</span><span data-if="dotnet" style="display:none">CreateDlLayerLossCrossEntropy</span><span data-if="python" style="display:none">create_dl_layer_loss_cross_entropy</span></a></code>, 
<code><a href="create_dl_layer_loss_focal.html"><span data-if="hdevelop" style="display:inline">create_dl_layer_loss_focal</span><span data-if="c" style="display:none">create_dl_layer_loss_focal</span><span data-if="cpp" style="display:none">CreateDlLayerLossFocal</span><span data-if="com" style="display:none">CreateDlLayerLossFocal</span><span data-if="dotnet" style="display:none">CreateDlLayerLossFocal</span><span data-if="python" style="display:none">create_dl_layer_loss_focal</span></a></code>, 
<code><a href="create_dl_layer_loss_huber.html"><span data-if="hdevelop" style="display:inline">create_dl_layer_loss_huber</span><span data-if="c" style="display:none">create_dl_layer_loss_huber</span><span data-if="cpp" style="display:none">CreateDlLayerLossHuber</span><span data-if="com" style="display:none">CreateDlLayerLossHuber</span><span data-if="dotnet" style="display:none">CreateDlLayerLossHuber</span><span data-if="python" style="display:none">create_dl_layer_loss_huber</span></a></code>
</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="set_dl_model_param.html"><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></a></code>
</p>
<h2 id="sec_module">模块</h2>
<p>
Deep Learning Training</p>
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